StatsClaw: An AI-Collaborative Workflow for Statistical Software Development

TL;DR

StatsClaw introduces a multi-agent AI workflow that enhances reliability in statistical software development by enforcing information barriers and independent validation, demonstrated on R and Python packages.

Contribution

It presents a novel multi-agent architecture for AI-assisted statistical software development that ensures faithful implementation and validation.

Findings

Successful end-to-end demonstration on a probit estimation package.

Effective application across three different statistical software packages.

Structured AI workflows can manage software lifecycle overhead while maintaining methodological control.

Abstract

Translating statistical methods into reliable software is a persistent bottleneck in quantitative research. Existing AI code-generation tools produce code quickly but cannot guarantee faithful implementation -- a critical requirement for statistical software. We introduce StatsClaw, a multi-agent architecture for Claude Code that enforces information barriers between code generation and validation. A planning agent produces independent specifications for implementation, simulation, and testing, dispatching them to separate agents that cannot see each other's instructions: the builder implements without knowing the ground-truth parameters, the simulator generates data without knowing the algorithm, and the tester validates using deterministic criteria. We describe the approach, demonstrate it end-to-end on a probit estimation package, and evaluate it across three applications to the authors' own R and Python packages. The results show that structured AI-assisted workflows can absorb the engineering overhead of the software lifecycle while preserving researcher control over every substantive methodological decision.